{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}\\cap\coombs\IPS1\redirections\u5390570\Desktop\Australian Public Opinion and Foreign Policy\Public Opinion on China Paper\IRAP Resubmission\Examining the Effects of the Division Level Variables Alone.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}11 Nov 2015, 14:05:43

{com}. meqrlogit china_threat export_prop || DivisNum:
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2824.8224}  (not concave)
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-2764.7556}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-2759.5586}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2759.5586}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-2741.1526}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-2739.7078}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-2739.7012}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res:-2739.7011}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 68}={col 70}{res}     3955
{txt}Group variable: {res}DivisNum{col 49}{txt}Number of groups{col 68}={col 70}{res}      150

{txt}{col 49}Obs per group: min{col 68}={col 70}{res}       11
{txt}{col 64}avg{col 68}={col 70}{res}     26.4
{txt}{col 64}max{col 68}={col 70}{res}       40

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}1{txt}){col 68}={col 70}{res}     0.05
{txt}Log likelihood = {res}-2739.7011{col 49}{txt}Prob > chi2{col 68}={col 73}{res}0.8272

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}china_threat{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}export_prop {c |}{col 14}{res}{space 2}-.2823182{col 26}{space 2} 1.293384{col 37}{space 1}   -0.22{col 46}{space 3}0.827{col 54}{space 4}-2.817305{col 67}{space 3} 2.252668
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.0525341{col 26}{space 2} .0409737{col 37}{space 1}   -1.28{col 46}{space 3}0.200{col 54}{space 4}-.1328411{col 67}{space 3}  .027773
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}DivisNum{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} 2.47e-11{col 44} 2.43e-06{col 58}        0{col 70}        .
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic regression:{col 34}{help j_chibar##|_new:chibar2(01) =}{col 48}{res}    0.00{col 57}{txt}Prob>=chibar2 = {col 73}{res}1.0000

{com}. meqrlogit china_threat mining_prop || DivisNum:
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2824.7506}  (not concave)
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-2764.6067}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-2761.9556}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2761.9556}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-2747.4215}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-2739.1003}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-2739.0961}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res:-2739.0961}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 68}={col 70}{res}     3955
{txt}Group variable: {res}DivisNum{col 49}{txt}Number of groups{col 68}={col 70}{res}      150

{txt}{col 49}Obs per group: min{col 68}={col 70}{res}       11
{txt}{col 64}avg{col 68}={col 70}{res}     26.4
{txt}{col 64}max{col 68}={col 70}{res}       40

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}1{txt}){col 68}={col 70}{res}     1.26
{txt}Log likelihood = {res}-2739.0961{col 49}{txt}Prob > chi2{col 68}={col 73}{res}0.2624

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}china_threat{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}mining_prop {c |}{col 14}{res}{space 2} 3.004441{col 26}{space 2} 2.680676{col 37}{space 1}    1.12{col 46}{space 3}0.262{col 54}{space 4}-2.249588{col 67}{space 3}  8.25847
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -.081347{col 26}{space 2} .0379475{col 37}{space 1}   -2.14{col 46}{space 3}0.032{col 54}{space 4}-.1557228{col 67}{space 3}-.0069712
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}DivisNum{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} 4.44e-15{col 44} 2.61e-08{col 58}        0{col 70}        .
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic regression:{col 34}{help j_chibar##|_new:chibar2(01) =}{col 48}{res}    0.00{col 57}{txt}Prob>=chibar2 = {col 73}{res}1.0000

{com}. meqrlogit china_threat agri_prop || DivisNum:
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2824.7744}  (not concave)
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-2764.6448}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-2759.6627}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2759.6627}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-2741.4485}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: -2739.209}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-2739.2035}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res:-2739.2035}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 68}={col 70}{res}     3955
{txt}Group variable: {res}DivisNum{col 49}{txt}Number of groups{col 68}={col 70}{res}      150

{txt}{col 49}Obs per group: min{col 68}={col 70}{res}       11
{txt}{col 64}avg{col 68}={col 70}{res}     26.4
{txt}{col 64}max{col 68}={col 70}{res}       40

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}1{txt}){col 68}={col 70}{res}     1.04
{txt}Log likelihood = {res}-2739.2035{col 49}{txt}Prob > chi2{col 68}={col 73}{res}0.3075

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   china_threat{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
agri_proportion {c |}{col 17}{res}{space 2}-1.776128{col 29}{space 2} 1.740642{col 40}{space 1}   -1.02{col 49}{space 3}0.308{col 57}{space 4}-5.187725{col 70}{space 3} 1.635468
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.0364239{col 29}{space 2} .0382829{col 40}{space 1}   -0.95{col 49}{space 3}0.341{col 57}{space 4}-.1114569{col 70}{space 3} .0386092
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}DivisNum{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} 4.90e-16{col 44} 8.74e-09{col 58}        0{col 70}        .
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic regression:{col 34}{help j_chibar##|_new:chibar2(01) =}{col 48}{res} 1.6e-11{col 57}{txt}Prob>=chibar2 = {col 73}{res}1.0000

{com}. meqrlogit china_threat exp_imp_ratio || DivisNum:
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2824.8059}  (not concave)
{res}{txt}Iteration 1:{space 3}log likelihood = {res: -2764.729}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-2760.4147}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2760.4147}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res: -2742.353}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-2739.6271}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res: -2739.626}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res: -2739.626}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 68}={col 70}{res}     3955
{txt}Group variable: {res}DivisNum{col 49}{txt}Number of groups{col 68}={col 70}{res}      150

{txt}{col 49}Obs per group: min{col 68}={col 70}{res}       11
{txt}{col 64}avg{col 68}={col 70}{res}     26.4
{txt}{col 64}max{col 68}={col 70}{res}       40

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}1{txt}){col 68}={col 70}{res}     0.20
{txt}Log likelihood = {res} -2739.626{col 49}{txt}Prob > chi2{col 68}={col 73}{res}0.6566

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} china_threat{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
exp_imp_ratio {c |}{col 15}{res}{space 2}-.0178362{col 27}{space 2} .0401166{col 38}{space 1}   -0.44{col 47}{space 3}0.657{col 55}{space 4}-.0964633{col 68}{space 3}  .060791
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}-.0483391{col 27}{space 2} .0387404{col 38}{space 1}   -1.25{col 47}{space 3}0.212{col 55}{space 4} -.124269{col 68}{space 3} .0275907
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}DivisNum{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} 3.36e-17{col 44} 2.78e-09{col 58}        0{col 70}        .
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic regression:{col 34}{help j_chibar##|_new:chibar2(01) =}{col 48}{res}    0.00{col 57}{txt}Prob>=chibar2 = {col 73}{res}1.0000

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}\\cap\coombs\IPS1\redirections\u5390570\Desktop\Australian Public Opinion and Foreign Policy\Public Opinion on China Paper\IRAP Resubmission\Examining the Effects of the Division Level Variables Alone.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}11 Nov 2015, 14:06:31
{txt}{.-}
{smcl}
{txt}{sf}{ul off}